Ryan Day

Ryan Day

Advanced Data Scientist at Conference of State Bank Supervisors

    Ryan Day is an advanced data scientist at the Conference of State Bank Supervisors (CSBS), a non-profit association in the financial services industry. At CSBS, Ryan supports state regulators by performing cloud software architecture, economics modeling, and advancing a data strategy. He is an AWS certified solutions architect and member of the National Association of Business Economics. He previously led the digital services division for a Federal agency, where he helped developers learn cloud development and adopt API standards. Ryan is an experienced open-source developer who participates in the FastAPI project. Ryan is currently writing a book titled "Hands-On APIs for AI and Data Science". It will be published in May 2025 by O'Reilly Publishing.

    All Sessions by Ryan Day

    Tutorials/Workshops West 07/23/2024

    Creating APIs That Data Scientists Will Love with FastAPI, SQLAlchemy, and Pydantic

    <span class="etn-schedule-location"> <span class="firstfocus">AI Engineering</span> <span class="secfocus">Beginner-Intermediate</span> </span>

    Data scientists and machine learning engineers are an important and growing API user base. But they’re not just another set of software developers – they have unique goals and tools. Ryan will share practical tips for making APIs that data scientists will love. Ryan will give a tour of the primary tasks that data scientists perform in their daily work and the tools in the Python ecosystem that they use such as Jupyter notebooks, Airflow, FastAPI, and Streamlit. Then he will discuss how those tools interact with APIs. The primary focus of the session will be demonstrating how to implement a data scientist-friendly API. The demonstrated APIs will use Python frameworks FastAPI for the API control, SQLAlchemy for data access, and Pydantic for data validation. In addition to the APIs, the session will demonstrate the value of Software Development Kits (SDKs) for data scientist usage. Attendees will learn to create APIs and SDKs using the demonstrated technologies.

    Using APIs in Data Science Without Breaking Anything

    <span class="etn-schedule-location"> <span class="firstfocus">AI Engineering</span> <span class="secfocus">Beginner-Intermediate</span> </span>

    Data scientists use APis in a variety of ways: as data sources for their data pipelines, to gather data for analytics products, and to consume machine learning models. The benefit of APIs is that they’re easy to use. The downside is that they’re also easy to misuse. How can data scientists design their systems to use APIs in a fault-tolerant way that can intelligently react to errors and ensure the validity of data that is provided? How can data scientists use APIs responsibly, without bringing down the API by accident? In this hands-on workshop, attendees will learn step-by-step the process of consuming a REST API in a Jupyter notebook. They will create fault-tolerant code that validates the output of APIs and handles errors intelligently. They will learn advanced techniques such as progressive backoff to avoid breaking the system they’re trying to call. Then they will learn how to make that code reusable by creating a software development kit (SDK) for the API.

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